Semantic Parsing Using Construction Categorization

Author(s):  
Yi Gao ◽  
Caifu Hong ◽  
Xihong Wu
Keyword(s):  
2014 ◽  
Author(s):  
Yoav Artzi ◽  
Dipanjan Das ◽  
Slav Petrov
Keyword(s):  

2015 ◽  
Author(s):  
Judith Gaspers ◽  
Philipp Cimiano ◽  
Britta Wrede

Author(s):  
Wenguan Wang ◽  
Tianfei Zhou ◽  
Siyuan Qi ◽  
Jianbing Shen ◽  
Song-Chun Zhu
Keyword(s):  

Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.


Author(s):  
Necva Bölücü ◽  
Burcu Can

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.


1980 ◽  
Author(s):  
Joachim H. Laubsch ◽  
Dietmar F. Roesner
Keyword(s):  

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